Bibliographic record
Abstract
This article deals with research into the importance of native or near native pronunciation of a person's L2 or L3 and the prejudices faced by those who may speak an accented form of a majority language.In order to reach its conclusions, the article analyses research focused on the learning of English as an L2 or L3 undertaken over the last 50 years.It concentrated on the impact of accented speech and the biases for and against accented speakers of a majority language and identifies where these findings converge and diverge.These results also indicate that the concerns of researchers have shifted as the perceptions of foreigners within English-speaking societies have changed over time.Ingroups are identified as important in determining the relative values of accents and that accented speakers constitute an audible minority and exert pressure on its members to maintain the "accentedness" of their speech.In defining the nature of an audible minority, the article indicates that this can be as powerful a factor as that of skin colour in some societies.In the end, it questions whether meticulous uniformity in spoken language is important or even desirable.The article concludes by suggesting that the problem of the audible minority is more that of the listener than the speaker since many of the myths surrounding accentedness and intelligibility have been proven false.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".